Particle filter algorithm based on geometric center and likelihood estimation
Fang, Xing1; Luo, Yin2,3; Wang, Lei2,3; Jiang, ShuiBin2,3; Xu, Nan2,3; Zeng, Daniel Dajun3,4,5
Source PublicationAIP ADVANCES
2021-08-01
Volume11Issue:8Pages:8
Corresponding AuthorLuo, Yin(yin.luo@wenge.com)
AbstractThe improved particle filter (PF) based on the geometric center and likelihood estimation is proposed in order to solve the problem of particle dilution and degradation. In the resampling stage, the geometric center is used to resample the particles. The particles are filtered according to the distance between the particles and the geometric center, and then the particles are resampled. The resampled particles are composed of newborn particles and non-resampled particles. The former can help alleviate the degradation problem, while the latter can keep the diversity of the particle set. In order to ensure effectiveness of the PF, the positioning error threshold of the particle filter is introduced. In the phase of particle weighting calculation, in view of the problem of low accuracy and divergence of PF state estimation caused by non-stationary and non-Gaussian noise, it adopts non-Gaussian noise parameter estimation based on likelihood to approximately estimate the measurement noise instead of the Gaussian density function. The proposed model is applied to particle weight calculation to avoid particle degradation caused by Gaussian density function approximation. The simulation results show that, after the improved algorithm, the root-mean-square error is reduced to 0.085, the variance is reduced to 0.014, and the running time is shortened by 14.8% compared with the polynomial resampling algorithm, which can effectively alleviate particle degradation and dilution in the traditional PF algorithm, and the positioning accuracy is also improved. (C) 2021 Author(s).
DOI10.1063/5.0058367
WOS KeywordRESAMPLING METHODS ; NEURAL-NETWORK ; KALMAN FILTER ; LOCALIZATION
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2016QY02D0305] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-XH-2017-3] ; National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[61671450] ; National Key Research and Development Program of China[2020AAA0103405] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27030100]
Funding OrganizationNational Key R&D Program of China ; Key Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences
WOS Research AreaScience & Technology - Other Topics ; Materials Science ; Physics
WOS SubjectNanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000692195300005
PublisherAIP Publishing
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/45945
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorLuo, Yin
Affiliation1.Tianjin Univ, Sch Management & Econ, Beijing, Peoples R China
2.Wenge Technol Co Ltd, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85001 USA
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Fang, Xing,Luo, Yin,Wang, Lei,et al. Particle filter algorithm based on geometric center and likelihood estimation[J]. AIP ADVANCES,2021,11(8):8.
APA Fang, Xing,Luo, Yin,Wang, Lei,Jiang, ShuiBin,Xu, Nan,&Zeng, Daniel Dajun.(2021).Particle filter algorithm based on geometric center and likelihood estimation.AIP ADVANCES,11(8),8.
MLA Fang, Xing,et al."Particle filter algorithm based on geometric center and likelihood estimation".AIP ADVANCES 11.8(2021):8.
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